Knowledge Sharing Platform

ABSTRACT

Disclosed is a non-transitory computer readable medium storing a computer program. The computer program performs operations for analyzing a video when the computer program is executed by one or more processors of a computing device and the operations may include: separating contents into one or more subcontents by analyzing the contents; matching and storing additional information with the subcontents; receiving search information from a user terminal; and sending at least one of the contents, the subcontents or the matched additional information corresponding to the search information to the user terminal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 120 and 37 CFR §1.78, as a Continuation in Part Application, to U.S. patent applicationSer. No. 15/220,311, which was filed in the United States Patent andTrademark Office on Jul. 26, 2016, the contents of which areincorporated herein by reference, in their entirety, for all purposes.

TECHNICAL FIELD

The present disclosure relates to a transaction method on the Internet,and more particularly, to a method for providing a platform capable oftransacting a product and a service based on knowledge.

BACKGROUND ART

In general, the Internet as an open network is configured to bearbitrarily connected and used by applying a common protocol calledTransmission Control Protocol/Internet Protocol (TCP/IP) to acounterpart computer which everybody intends to access anywhere in theworld can use various services such as e-mail, file transmission, WorldWide Web (WWW), and the like which are used for transferring basic textinformation, developing a compression technique, and transferringmultimedia information.

The importance of the Internet as a strategic tool for promotingefficiency and productivity throughout all fields of the existingindustry has rapidly increased as the Internet is rapidly increasinglyused worldwide including in Korea and new business opportunities throughthe Internet have been continuously created and areas of business havetended to extend, and as a result, business operators using the Internethave gradually increased.

Mobile Internet using a cellular phone, a PDA, and an IMT2000 servicehas been rapidly increasingly used in recent years and the service hasbeen explosively increased as fast as the initial increase speed ofInternet users.

That is, in recent years, businesses through the Internet have repeateddevelopment to create a more improved new business model such as Avatartransaction and a question and answer type knowledge search service overbusiness models including Internet search, shopping mall, an auction,and the like in an initial stage.

In the related art, Internet shopping malls are configured for each itemor item category and consumers individually search information on theitem and the service according to a process for purchase wants thereofto determine the item and the service and thereafter, satisfy thepurchase wants by using a purchase site such as the shopping mall, orthe like. However, the search time for determining the item and theservice is long and it is difficult even to acquire information on astore. Further, since a purchase intention for a special sold item orservice should be decided only on a limited site, there is a problem inthat it is difficult to provide a definite criterion for comparing aprice or the service.

Accordingly, the consumer can ask a question about such a complicatedprocess on the Internet and perform such a purchase action according toan answer to the question.

SUMMARY OF THE INVENTION

The present disclosure has been made in an effort to provide additionalknowledge for respective processes included in an answer in a knowledgeservice.

The present disclosure has also been made in an effort to easily searcha desired item and knowledge only by a category and an object forinformation search without going through a complicated process in orderto find an item and knowledge desired by a consumer.

An exemplary embodiment of the present disclosure provides anon-transitory computer readable medium storing a computer program. Thecomputer program performs operations for analyzing contents when thecomputer program is executed by one or more processors of a computingdevice and the operations may include: separating contents into one ormore subcontents by analyzing the contents; matching and storingadditional information with the subcontents; receiving searchinformation from a user terminal; and sending at least one of thecontents, the subcontents or the matched additional informationcorresponding to the search information to the user terminal.

Another exemplary embodiment of the present disclosure provides a methodfor analyzing contents. The method may include: separating contents intoone or more subcontents by analyzing the contents; matching and storingadditional information with the subcontents; receiving searchinformation from a user terminal; and sending at least one of thecontents, the subcontents or the matched additional informationcorresponding to the search information to the user terminal.

Still another exemplary embodiment of the present disclosure provides aserver for analyzing contents. The server may include: a processorincluding one or more cores; and a memory, in which the processor may beconfigured to separate contents into one or more subcontents byanalyzing the contents; match and store additional information with thesubcontents; receive search information from a user terminal; and sendat least one of the contents, the subcontents or the matched additionalinformation corresponding to the search information to the userterminal.

The present disclosure can provide additional knowledge for respectiveprocesses included in an answer in a knowledge service.

The present disclosure can easily search a desired item and knowledgeonly by a category and an object for information search without goingthrough a complicated process in order to find an item and knowledgedesired by a consumer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a server for providing knowledge basede-commerce according to an exemplary embodiment of the presentdisclosure.

FIG. 2 is a flowchart of a method for providing knowledge basede-commerce according to an exemplary embodiment of the presentdisclosure.

FIG. 3 illustrates an example of an analysis data table of a questionand an answer which are collected according to an exemplary embodimentof the present disclosure.

FIG. 4 illustrates an example of analysis of an answer according to anexemplary embodiment of the present disclosure.

FIG. 5 illustrates a more detailed example of analysis of an answeraccording to an exemplary embodiment of the present disclosure.

FIG. 6 illustrates an example of additional information of an analyzedanswer according to an exemplary embodiment of the present disclosure.

FIG. 7 is a block diagram of a computer which performs an operation ofexecuting a computer program for providing knowledge based e-commerceaccording to an exemplary embodiment of the present disclosure.

FIG. 8 is a schematic block diagram of an exemplary computingenvironment that executes a computer program for providing knowledgebased e-commerce according to an exemplary embodiment of the presentdisclosure.

FIG. 9 illustrates an example of subcontents according to an exemplaryembodiment of the present disclosure.

FIG. 10 illustrates an example of additional information according to anexemplary embodiment of the present disclosure.

FIG. 11 illustrates an example of a feedback according to an exemplaryembodiment of the present disclosure.

FIG. 12 illustrates an example of a search method according to anexemplary embodiment of the present disclosure.

FIG. 13 illustrates an example of contents and subcontents according toan exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

Various exemplary embodiments and/or aspects are now disclosed withreference to drawings. In the following description, for description,multiple detailed matters are disclosed in order to help overallunderstanding of one or more aspects. However, those skilled in the artwill recognize even that the aspect(s) can be executed without thedetailed matters. In the following disclosure and the accompanyingdrawings, specific exemplary aspects of one or more aspects will bedescribed in detail. However, the aspects are exemplary and some amongvarious methods in principles of various aspects may be used and thedescriptions are intended to include all of the aspects and equivalentsthereof.

Further various aspects and features will be presented by a system whichcan include multiple devices, components, and/or modules. It should alsobe appreciated and recognized that various systems can includeadditional devices, components, and/or modules and/or that the varioussystems cannot include all of devices, components, modules, and the likediscussed in association with the drawings.

In “embodiment”, “example”, “aspect”, “illustration”, and the like usedin the specification, it may not be construed that a predeterminedaspect or design which is described is more excellent or advantageousthan other aspects or designs. ‘Component’, ‘module’, ‘system’,‘interface’, and the like which are terms used below generally meancomputer-related entities and mean, for example, hardware, a combinationof hardware and software, or software.

The term “or” is intended to mean not exclusive “or” but inclusive “or”.That is, when not separately specified or not clear in terms of context,the case where “X uses A or B” is intended to mean one of naturalinclusive substitutions. That is, in the case where “X uses A or B” maybe applied to either of the case where X uses A, the case where X usesB, or the case where X uses both A and B. Further, it should beunderstood that the term “and/or” used in the specification designatesand includes all available combinations of one or more items amongenumerated related items.

The word “comprises” and/or “comprising” means that the correspondingfeature and/or component is present, but it should be appreciated thatpresence or inclusion of one or more other features, components, and/ora group thereof is not excluded. Further, when not separately specifiedor not clear in terms of the context by indicating a singular form, itshould be construed that the singular generally means “one or more” inthe present specification and the claims.

FIG. 1 is a block diagram of a server for providing knowledge basede-commerce according to an exemplary embodiment of the presentdisclosure.

The server 100 for providing knowledge based e-commerce according to theexemplary embodiment of the present disclosure includes one or moreprocessors (not illustrated) and a memory (not illustrated) storingcommands which may be executed by the processor and the processor mayinclude a collection module 110, a process extracting module 130, aprocess processing module 150, a communication module 170, and acommunication module 190.

The collection module 110 may collect a question and an answer for thequestion from a knowledge sharing platform. The knowledge sharingplatform of the present disclosure may be a platform in which users mayexchange information. The knowledge sharing platform of the presentdisclosure may be a platform in which the users provide at least one ofa question or an answer. For example, the knowledge sharing platform maybe a platform in which one user may upload contents (e.g., text, images,video, etc.) and other users may respond to the contents. According toan exemplary embodiment of the present disclosure, the contents that theuser uploads to the knowledge sharing platform may include, for example,images, video, voice, text, and the like. The user who asks the questionand the user who gives the answer in the knowledge sharing platform maybe one user or two or more different users.

The knowledge sharing platform of the present disclosure may be aplatform of a type in which a user arbitrarily asks a question and ananswerer arbitrarily answers a question. A plurality of unspecific usersmay make a question and perform the answer for the question by takingpart and the questions and the answers are accumulated to be used asknowledge data. The knowledge sharing platform may be a knowledgesharing platform in which the answer is input to be separated for eachprocess. For example, the knowledge sharing platform may be a platformthat allows one user to upload the question and the answer or may be aplatform that allows another user to upload the answer corresponding tothe question when one user uploads the contents regarding the question.For example, the knowledge sharing platform may be a video sharingplatform. The video sharing platform may allow videos related to thequestion and the answer to be uploaded. Alternatively, the video sharingplatform may allow the videos related to the question or the answer tobe uploaded and allow the question or the answer to be uploaded througha feedback (e.g., comments, expression of likes and dislikes about thevideo, etc.) for the uploaded video. The video sharing platform mayallow various videos without a limit in format to be uploaded. Forexample, when a questioner questions a task which may be processed foreach step, the answerer may make an answer for each step and theknowledge sharing platform may be a knowledge sharing platform whichseparates the answer for each process by a column and a partition so asto receive the answer for each step. Therefore, the collection module110 may collect the answer separated for each process when collectingthe answer separated by the column and the partition. For example,various contents may be uploaded to the knowledge sharing platform. Forexample, the videos may be uploaded to the knowledge sharing platformand the contents of the video may be a description of a specific task.For example, a video including the question or the answer may beuploaded for each step for a task which may be processed for each stepor the question or the answer may be uploaded as the feedback for theuploaded video, to the knowledge sharing platform. For example, theknowledge sharing platform may display a time interval for the questionor answer for each step in the video. For example, the knowledge sharingplatform may allow an indication that the question and the answer forstep A are performed from 0 minute and 50 seconds to 1 minute and 24seconds of the video to be included in the video or an additionaldescription of the video. Further, for example, the knowledge sharingplatform may indicate for which step of question the correspondinganswer is an answer (i.e., the indication that the question and theanswer are performed from 0 minute and 50 seconds to 1 minute and 24seconds of the video) at the time of uploading the answer as thefeedback for the answer for step A included in the video. A disclosureassociated with separating the answer for each process in the knowledgesharing platform described above is just an example and the answer maybe received while being separated for each process by a predeterminedmethod in the knowledge sharing platform. The collection module 110 mayarbitrarily collect the questions of the users and the answers for thequestions from the knowledge sharing platform. The knowledge sharingplatform may be included in the server 100 according to the exemplaryembodiment of the present disclosure. Further, the knowledge sharingplatform may be included in a separate knowledge sharing server otherthan the server 100 of the present disclosure. The collection module 110of the present disclosure may collect the questions and the answers of aknowledge sharing platform included in the server 100 and collectquestions and answers of a knowledge sharing platform included in anexternal server. Further, the collection module 110 may collectquestions and answers of the knowledge sharing platform which is presenton the Internet and online. The questions and the answers according tothe exemplary embodiment of the present disclosure may include at leastcontents associated with e-commerce. The contents associated withe-commerce may include contents associated with purchase, selling andmanufacturing of items or purchasing and selling of services. Adisclosure of the contents associated with e-commerce described above isjust an example and the present disclosure may include contentsassociated with predetermined e-commerce.

The collection module 110 may collect the question or the answer in theknowledge sharing platform by using a deep learning algorithm. Thecollection module 110 may analyze the video including the question oranswer uploaded to the knowledge sharing platform by using the deeplearning algorithm and collect the analyzed video.

The collection module 110 may identify the contents uploaded to theknowledge sharing platform. The contents may be various information orcontents for producing, processing, or distributing texts, codes, voice,sounds, images, videos, etc., in a digital scheme for use in wired andwireless telecommunication networks. For example, the contents uploadedto the knowledge sharing platform may include at least one of the videoor query response texts as described above.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may separate contents into subcontentsbased on a context. For example, the process extracting module 130 mayseparate the videos based on the context of the video. For example, theprocess extracting module 130 may divide the video related to the taskwhich may be processed for each process for each process. For example,the process extracting module 130 may separate the video including thequestion or answer for each process related to the task which may beprocessed for each step for each process. The process of the presentdisclosure may include, for example, each step of the task. For example,in a task of removing rust from a rusty tool, the process may be, forexample, a chemical treatment step. In this case, for example, in thecontents related to the task, the subcontents may include one or moreprocesses. The aforementioned task and process are just an example andthe present disclosure is not limited thereto. In the presentdisclosure, in the contents related to the task, the subcontents may beconstituted by one or more processes for performing the task and theprocess may include one or more steps of the task.

The process extracting module 130 may divide the video into differentsteps based on a portion where the context of the contents included inthe video is changed. The process extracting module 130 may determinethat the context of the contents included in the video is changed, forexample, based on a change of at least one object included in the video.The process extracting module 130 may divide the video before at leastone object is changed and the video after at least one object is changedand determine the videos as different steps. The process extractingmodule 130 may analyze a similarity between pixels included in a firstframe and pixels included in a second frame to determine that thecontext of the contents included in the video is changed if thesimilarity is less than a predetermined threshold value. For example, inthe case of an answer video for a question for a cosmetic product, theprocess extracting module 130 may determine that a portion where a brushobject appears and a portion where a blusher object appears a partcorrespond to the answers of different steps. A detailed description ofthe collection module is just an example and the present disclosure isnot limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may analyze the video by using a videoanalysis algorithm. For example, the process extracting module 130 mayanalyze the video including the question or answer by using a videoprocessing algorithm or a voice processing algorithm.

For example, the process extracting module 130 may input the videouploaded to the knowledge sharing platform, identify at least one objectincluded in the video, and separate the identified object from thevideo. The object separated from the video may be an objectcorresponding to the question or the answer. For example, when alipstick of a specific brand is described by the answer in the questionvideo for the cosmetic product, a lipstick object identified in thevideo may be collected as the answer. The video processing algorithm ofthe process extracting module 130 may include canny edge detection,Harris corner detection, and the like, but the present disclosure is notlimited thereto. The process extracting module 130 performs blurringprocessing of the video through the canny edge detection to removenoise, detects an edge by using a mask edge, removes a Non-MaximumValue, and connects the edge by distinguishing a size with a doublethreshold to identify at least one object included in the video. Thedetailed description of the collection module is just an example and thepresent disclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may analyze the video including thequestion or answer by using a natural language processing algorithm. Theprocess extracting module 130 may analyze a caption or voice included inthe video using the natural language processing algorithm and collectthe question or answer included in the video. The process extractingmodule 130 may analyze the feedback for the video and collect thequestion or answer. For example, the process extracting module 130 mayanalyze the comment of the video, which is the feedback for the videoand collect the question or answer. The detailed description of thecollection module is just an example and the present disclosure is notlimited thereto. For example, the process extracting module 130 mayseparate the videos uploaded to the knowledge sharing platform for eachstep. For example, if the video is a video in which the rust is removedfrom the rusted tool, the process extracting module 130 may analyze thevideo and separate the video for each step for removing the rust. Forexample, when text information matches the video (e.g., a sandpaperstep, a chemical treatment step, etc.), the process extracting module130 may separate the video for each step based on the matching of thevideo and the text information. The aforementioned description is justan example and the present disclosure is not limited thereto.

The process extracting module 130 may extract a process for solving thequestion from the answer by analyzing the collected answer. An operationof extracting the process for solving the question from the answeraccording to an exemplary embodiment of the present disclosure may meanan operation of separating the answer into one or more processes andstoring the separated processes in a storage space. For example, whenthe answer for the question includes contents for describing the processfor solving the question, the process extracting module 130 may extractthe process for solving the question from the answer by analyzing thecollected answer.

Further, the process extracting module 130 may determine a category ofthe collected answer and tag an object to the answer. In addition, theprocess extracting module 130 may determine a category of the collectedquestion and tag the object to the question. For example, the processextracting module 130 may determine the category of the objectcorresponding to the question or answer contained in the video and tagthe object on the object. Alternatively, the process extracting module130 may determine the category of the question or answer included in thecomments as the feedback for the video and tag the object on thequestion or answer included in the comment. An operation of tagging theobject according to an exemplary embodiment of the present disclosuremay mean an operation of matching the object corresponding to thequestion or answer with the question or answer and storing the matchedobject in the storage space. The detailed description of the processextracting module is just an example and the present disclosure is notlimited thereto.

The process extracting module 130 may collect feedback information ofthe questioner for the answerer of each answer, determine reliability ofthe answerer based on the collected feedback information, extract akeyword by collecting an answer of an answerer in which the reliabilityis equal to or higher than a predetermined threshold, and separate theanswer into one or more processes at least partially based on theextracted keyword. Collection according to an exemplary embodiment ofthe present disclosure may mean an operation of identifying dataincluded in the knowledge sharing platform and separately storing thedata in the storage space. In the knowledge sharing platform, thequestioner may feed back whether the corresponding answer becomes theanswer for the question with respect to the answer of the answerer. Forexample, when the answer of the answerer plays a decisive role insolving the question with respect to matters questioned by thequestioner, the questioner may give a feedback such as high satisfactionor scoring with respect to the corresponding answer and answerer. Thedetailed description of the feedback is just an example and the presentdisclosure is not limited thereto.

The process extracting module 130 may identify the feedback informationfor the contents and determine the reliability of the contents based onthe feedback information for the contents. The process extracting module130 may separate contents having reliability equal to or higher than apredetermined threshold into the subcontents.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may check data in which the user who viewsthe contents gives feedback such as affirmation (i.e., satisfaction) ordenial (i.e., dissatisfaction) with respect to the contents. Forexample, the feedback may be data acquired by clicking on an interfaceobject indicating affirmation or denial for the contents or dataregarding the comments for the contents. For example, the processextracting module 130 may determine the reliability of the contents byanalyzing the feedback, which is the data in which the user clicks on aninterface object (i.e., feedback) for each of “like” or “dislike” forthe contents. Further, the process extracting module 130 may determinethe reliability of the contents by analyzing the comments (i.e.,feedback) for the contents. For example, the process extracting module130 may determine that the reliability of knowledge included in thecontents is high when the number of affirmation feedbacks (e.g.,indication of “like” for the video) for the contents is equal to or morethan a predetermined threshold. Alternatively, the process extractingmodule 130 may determine that the reliability of the knowledge includedin the contents is low when the number of denial feedbacks (e.g.,indication of “dislike” for the video) for the contents is equal to ormore than a predetermined threshold. The process extracting module 130may determine the reliability of the knowledge included in the contentsbased on a ratio of the affirmation feedbacks and the denial feedbacksfor the contents. The process extracting module 130 may determine thereliability of the knowledge included in the contents by analyzing thecomments (i.e., feedback) for the contents. The process extractingmodule 130 may determine whether the feedback for the video isaffirmation or denial based on a keyword included in the comments forthe contents. The detailed description of the feedback is just anexample and the present disclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may check data in which the user gives thefeedback such as affirmation or denial with respect to the knowledgeincluded in the comments for the contents. For example, an indicationsuch as affirmation or denial for the comments on the contents may alsobe included in feedback data for determining the reliability of thecontents. For example, the feedback may be data acquired by clicking onthe interface object indicating affirmation or denial for the contentsor data regarding the comments for the contents. For example, theprocess extracting module 130 may determine the reliability of theanswer included in the comments for the contents by analyzing thefeedback, which is the data in which the user clicks on the interfaceobject (i.e., feedback) for each of “like” or “dislike” for the commentsfor the contents. The process extracting module 130 may determine thereliability of the answer included in the comments by analyzing thecomments (i.e., feedback) for the comments for the contents. The processextracting module 130 may determine whether the feedback for thecomments is affirmation or denial based on the keyword included in thecomments for the comments. The detailed description of the feedback isjust an example and the present disclosure is not limited thereto.

The process extracting module 130 collects the feedback information anddetermines the reliability of the answerer based on the feedbackinformation. The reliability is determined for each answerer and theanswerer for whom the reliability is equal to or higher than thethreshold may be determined as an answerer who is reliable.

The process extracting module 130 may determine the reliability for aproducer of the contents based on the feedback information for theproducer of the contents. The process extracting module 130 may separateat least some of the contents of the producer having reliability whichis equal to or more than a predetermined threshold into the subcontents.For example, the process extracting module 130 may collect the feedbackinformation for each producer who uploads the contents and determine thereliability for the producer. Alternatively, the process extractingmodule 130 may collect the feedback information for each account of theknowledge sharing platform and determine the reliability for a user ofthe account. For example, when the process extracting module 130determines that the reliability of the account user who uploads thevideo is high based on the feedback for the video, if the comments whichthe account user creates for another video include the answer, it may bedetermined that the reliability of the corresponding answer is high. Thedetailed description of the feedback is just an example and the presentdisclosure is not limited thereto.

The process extracting module 130 may extract a keyword or an objectfrom the answers by collecting the answers of the reliable answerers.Additionally, for example, a fuzzy algorithm may be used for the keywordextraction and the process extracting module 130 may extract the keywordbased on the fuzzy algorithm. The process extracting module 130 mayextract the object based on the video processing algorithm. The processextracting module 130 may separate the answer into one or more processesat least partially based on the extracted keyword or object. Thedetailed description of the process extracting module is just an exampleand the present disclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may separate the answer into one or moreprocesses at least partially based on the keyword and the number ofrepetition times of a keyword associated with the keyword in the answer.For example, when the question is associated with purchasing of a usedcar, the answer may be separated into processes including “a used carsearching step”, “a used car complex visiting step”, “a vehicle checkingstep before a contract”, “a contract step”, “a transfer step”, and thelike. In this case, when keywords including “a service station”, “aninterior and an exterior”, and the like are present with respect tovehicle checking, a sentence or paragraph including the correspondingkeyword in the answer may be separated into the “checking step beforethe contract” process. The question, the answer, the process, and thekeyword described above are just examples and the present disclosureincludes a predetermined question, a predetermined answer, apredetermined process, and a predetermined keyword.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may separate the answer into one or moreprocesses at least partially based on at least one object, a keywordcorresponding to the object, or the number of repetition times of akeyword associated with the object in the answer. For example, when thequestion is associated with an overseas travel exit method, the contentsincluding the answer for the question may be a video regarding anoverseas travel exit process. In this case, the process extractingmodule 130 separates the objects included in the video to separate theprocesses into processes including “a check-in step” corresponding to anobject for a check-in counter, “departure hall waiting step”corresponding to objects such as a passport, an airplane ticket, and aplurality of persons who stands in line, “a security check point passingstep” corresponding to an object for a security check point, and “adeparture examination step” corresponding to an object for recognitionof a fingerprint, a face, and the passport. The question, the answer,the process, the keyword, and the object described above are justexamples and the present disclosure includes a predetermined question, apredetermined answer, a predetermined process, a predetermined keyword,and a predetermined object.

The process extracting module 130 may collect feedback information forthe answer, determine the reliability of the answer based on thecollected feedback information, extract the keyword by collecting ananswer in which the reliability is equal to or higher than apredetermined threshold, and separate the answer into one or moreprocesses at least partially based on the extracted keyword. In theknowledge sharing platform, the questioner may feedback whether theanswer becomes the answer for the question. For example, when the answerof the answerer plays a decisive role in solving the question withrespect to matters questioned by the questioner, the questioner may givea feedback such as high satisfaction or scoring with respect to thecorresponding answer. The process extracting module 130 collects thefeedback information and determines the reliability of the answer basedon the feedback information. The reliability is determined for eachanswer and an answer for which the reliability is equal to or higherthan the threshold may be determined as an answer which is reliable. Theprocess extracting module 130 may extract a keyword from the answers bycollecting the reliable answers. Additionally, for example, a fuzzyalgorithm may be used for the keyword extraction and the processextracting module 130 may extract the keyword based on a fuzzyalgorithm.

Hereinafter, a method in which the processor extracting module 130identifies contents having the reliability which is equal to or morethan a threshold will be described.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may separate the contents having thereliability which is equal to or more than the threshold among thecontents identified using the collection module 110 into one or moresubcontents.

The process extraction module 130 may identify the feedback informationfor the contents. The process extracting module 130 may determine thereliability for the contents based on the feedback information.

Hereinafter, the feedback will be described with reference to FIG. 11.FIG. 11 illustrates an example of a method for determining reliabilityaccording to an exemplary embodiment of the present disclosure. FIG. 11includes a content 1310 displayed on an interface of a user terminal anda screen 1300 displaying a feedback means according to an exemplaryembodiment of the present disclosure.

The feedback information for the content 1310 according to an exemplaryembodiment of the present disclosure may include data in which aselection input for an interface object indicating affirmation 1330 ordenial 1340 for the content 1310 is received from the user terminalusing the content 1310. When one user uploads the content 1310, theknowledge sharing platform may provide the user terminal with aninterface object that allows other users to give the feedback for thecontent 1310. The interface object that may allow the users to give thefeedback may be, for example, an interface object indicating theaffirmation 1330 or the denial 1340. The detailed description of thefeedback is just an example and the present disclosure is not limitedthereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the reliability for thecontent 1310 based on the number of selection inputs for the interfaceobject indicating the affirmation 1330 or the denial 1340. According toan exemplary embodiment of the present disclosure, the processextracting module 130 may determine that the reliability for the content1310 is high when the number of selection inputs for the interfaceobject indicating the affirmation 1330 is equal to or more than apredetermined value. According to another exemplary embodiment of thepresent disclosure, the process extracting module 130 may determine thatthe reliability for the content 1310 is low when the number of selectioninputs for the interface object indicating the denial 1340 is equal toor more than a predetermined value. According to another exemplaryembodiment of the present disclosure, the process extracting module 130may determine the reliability based on the ratio of the number ofselection inputs for the interface object indicating the affirmation1330 and the number of selection inputs for the interface objectindicating the denial 1340. For example, the process extracting module130 may determine that the reliability for the content 1310 is high whenthe number of selection inputs for the interface object indicating theaffirmation 1330 is equal to or larger than and the number of selectioninputs for the interface object indicating the denial 1340 by apredetermined ratio or more. The detailed description of the reliabilitydetermination is just an example and the present disclosure is notlimited thereto.

According to an exemplary embodiment of the present disclosure, thefeedback information for the content 1310 include data in which thecontents 1310 are shared or stored by another user. An operation ofsharing the content 1310 by another user may mean an operation ofsending a link address to access the content 1310 to another user. Whenone user uploads the content 1310, the knowledge sharing platform mayprovide the user terminal with an interface object that allows otherusers to share or store the corresponding content 1310 for thecorresponding content 1310. The interface object that may allow theusers to give the feedback may be, for example, an interface objectindicating sharing 1350 or storing 1360. The detailed description of thefeedback is just an example and the present disclosure is not limitedthereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the reliability for thecontent 1310 based on the number of selection inputs for the interfaceobject indicating the sharing 1350 or the storing 1360. According to anexemplary embodiment of the present disclosure, the process extractingmodule 130 may determine that the reliability for the content 1310 ishigh when the number of selection inputs for the interface objectindicating the sharing 1350 or the storing 1360 is equal to or more thana predetermined value. The detailed description of the reliabilitydetermination is just an example and the present disclosure is notlimited thereto.

In an exemplary embodiment of the present disclosure, the reliabilityfor the contents may be determined based on the number of inquiries forthe contents. The process extracting module 130 may determine thereliability for the contents based on the number of inquires for thecontents, a reproduction time of respective users, and the like.

According to an exemplary embodiment of the present disclosure, thefeedback information for the content 1310 may include text data 1380described regarding the content 1310 from the user terminal using thecontents. When one user uploads the content 1310, the knowledge sharingplatform may provide an interface object to create the text data 1380 sothat other users remain an opinion for the content 1310 with respect tothe corresponding content 1310. The knowledge sharing platform mayreceive the text data 1380 about the affirmation or the denial withrespect to the content 1310 from the user terminal. The detaileddescription of the feedback is just an example and the presentdisclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the reliability of thecontent 1310 based on the text data 1380 received from the userterminal.

According to an exemplary embodiment of the present disclosure, theprocess extraction module 130 may determine the reliability of thecontent 1310 based on a quantitative value of the text data 1380received from the user terminal. For example, the process extractingmodule 130 may determine that the reliability of the content 1310 ishigh when a number 1370, which is a quantitative value of the text data1380 received from the user terminal, is equal to or more than apredetermined value. The detailed description of the reliabilitydetermination is just an example and the present disclosure is notlimited thereto.

According to another exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the reliability of thecontent 1310 based on contents of the text data 1380 received from theuser terminal. The process extracting module 130 may determine thereliability of the content 1310 based on the keyword included in thetext data 1380. The process extracting module 130 compares the keywordsincluded in the text data 1380 with an affirmation keyword and a denialkeyword stored in the storage space in advance to determine thereliability of the content 1310 based on the ratio of the affirmationkeyword and the denial keyword included in the text data 1380. Theprocess extracting module 130 compares the keywords included in the textdata 1380 with the affirmation keyword and the denial keyword stored inadvance in the storage space to determine that the reliability of thecontent 1310 is high when the number of affirmation keywords included inthe text data 1380 is equal to or more than a predetermined number oftimes or determine that the reliability of the content 1310 is low whenthe number of denial keywords included in the text data 1380 is equal toor more than a predetermined number of times. For example, whenaffirmation keywords including “like”, “good”, “luv”, and the like arederived from the text data 1380 at a predetermined number of times ormore, it may be determined that the reliability for the content 1310 ishigh. Alternatively, the process extracting module 1310 may determinewhether the feedback for the text data 1380 is affirmative or denial byprocessing of a natural language of the text data 1380. Alternatively,when determining the reliability based on two or more text data 1380,the process extracting module 130 gives different weights for each textdata 1380 based on the feedback 1390 for the text data 1380 to determinethe reliability. The process extracting module 130 may exclude the textdata 1380 from the reliability determination of the content 1310, forexample, when the feedback 1390 for the text data 1380 is denial. Theprocess extracting module 130 may determine the reliability of thecontent 1310 by performing calculation by giving a higher weight to thetext data 1380, for example, when the feedback 1390 for the text data1380 is affirmative. The detailed description of the reliabilitydetermination is just an example and the present disclosure is notlimited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the reliability for thecontent 1310 based on the number of views 1320 for the users for thecontent 1310 or the number of feedbacks of the users. The number offeedbacks of the users may be, for example, the number of recordingtimes of the text data 1380 of the users, the number of selection inputsof the interface object indicating the affirmation 1330 or the denial1340, the number of times of sharing 1350 or storing 1360, etc. Theprocess extracting module 130 may determine that the reliability for thecontent 1310 is high when the number of views 1320 of the users for thecontent 1310 or the number of feedbacks of the users is equal to or morethan a predetermined value.

The process extracting module 130 may separate contents havingreliability equal to or higher than a predetermined threshold into oneor more subcontents. According to an exemplary embodiment of the presentdisclosure, the process extracting module 130 may separate contentshaving reliability which corresponds to a predetermined high rank orcontents having reliability equal to or higher than a predeterminedvalue among a plurality of contents identified by the collection module110 into one or more subcontents. A method for separating the contentsinto one or more subcontents will be described below in detail.

According to another exemplary embodiment of the present disclosure, theprocess extracting module 130 may separate the contents in which thereliability for the producer of the contents is equal to higher than athreshold among the contents identified by using the collection module110 into one or more subcontents.

The process extraction module 130 may identify the feedback informationfor the producer of the contents. The process extracting module 130 maydetermine the reliability for the producer for the contents based on thefeedback information.

According to an exemplary embodiment of the present disclosure, thefeedback information for the producer of the contents may be affirmativeor denial feedback information for the producer of the contents. Theaffirmative feedback information for the content producer may be, forexample, a quantitative number of subscribers who subscribe the contentproducer, an increase rate of the number of subscribers who subscribethe content producer, the number of user terminals set so that an alarmis set in the user terminal when the content producer uploads newcontents, and the number of affirmative comments in an Internetcommunity for the content producer. The denial feedback information forthe content producer may be, for example, a decrease rate of the numberof subscribers who subscribe the content producer and the number ofdenial comments in the Internet community for the content producer. Thedetailed description of the feedback information for the contentproducer is just an example and the present disclosure is not limitedthereto.

The process extracting module 130 may determine that the reliability ofthe content producer is equal to or higher than a threshold when theaffirmative feedback information for the content producer is equal to ormore than a predetermined threshold. The process extracting module 130may determine that the reliability of the content producer is equal toor lower than a threshold when the denial feedback information for thecontent producer is equal to or more than a predetermined threshold. Theprocess extracting module 130 may determine the reliability for thecontent producer based on the ratio of the affirmative feedbackinformation and the denial feedback information for the contentproducer. For example, the process extracting module 130 may determinethat the reliability of the content producer is high when theaffirmative feedback information for the content producer is higher thanthe denial feedback information at a predetermined ratio or higher.

The process extracting module 130 may separate at least some of thecontents of the producer having the reliability equal to or more thanthe predetermined threshold into one or more subcontents. According toan exemplary embodiment of the present disclosure, the processextracting module 130 may separate all of the contents of the producerhaving the reliability equal to or higher than the predeterminedthreshold into one or more subcontents. According to an exemplaryembodiment of the present disclosure, the process extracting module 130may separate some contents of the contents of the producer having thereliability equal to or higher than the predetermined threshold into oneor more subcontents. The process extraction module 130 may separate onlycontents of high rank in which the reliability for the contents ispredetermined or contents having the reliability for the contents equalto or higher than a predetermined value among the contents of theproducer having the reliability equal to or higher than a predeterminedthreshold into the subcontents. The detailed description of the contentselection is just an example and the present disclosure is not limitedthereto.

Hereinafter, the method for separating the contents into one or moresubcontents will be described.

The process extracting module 130 may separate contents identified byusing the collection module 110 into one or more subcontents. Thesubcontents may mean at least some contents included in the contents.The contents may include one subcontent or may include two or moresubcontents. The subcontents may mean some of processes included in thecontents or mean some of objects included in the contents.

A method in which the process extracting module 130 separates thecontents into one or more subcontents will be described with referenceto FIG. 9. FIG. 9 illustrates an example of subcontents according to anexemplary embodiment of the present disclosure.

The process extracting module 130 may separate the contents into two ormore subcontents based on a point at which the context of the contentsis changed. The process extracting module 130 may determine contentsbefore the point at which the context of the contents is changed as firssubcontents and contents after the point at which the context of thecontents is changed as second subcontents. The first subcontents and thesecond subcontents may be contents related to different categories.Alternatively, the first subcontents and the second subcontents may becontents related to different objects.

The category according to an exemplary embodiment of the presentdisclosure may be information indicating which category the contentsbelong to. The category may be, for example, a beauty, an automobile, atrip, a work, an exercise, etc., but this is merely an example and thepresent disclosure is not limited thereto.

The object according to an exemplary embodiment of the presentdisclosure may be information indicating which object or object thecontents are for. The object may be, for example, a blush, a lipstick, aused car trading method, a contract writing method, etc, but this ismerely an example and the present disclosure is not limited thereto.

For example, both first subcontents 910 and second subcontents 920 maybelong to a beauty category and the objects may be different as theblush and the lipstick, respectively. Alternatively, the firstsubcontents and the second subcontents may be different as the beautycategory and the automobile category, respectively. The detaileddescription of the subcontents is just an example and the presentdisclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the point at which thecontext of the contents is changed by using the video processingalgorithm.

The process extracting module 130 may analyze the video included in thecontents by using the video processing algorithm. The process extractingmodule 130 may analyze the video included in the contents by using avideo processing model including one or more network functions. Thevideo processing model may be a model based on a Convolution NeuralNetwork (CNN). Alternatively, the video processing algorithm may includecanny edge detection, Harris corner detection, and the like. Theconcrete description of the video processing method is just an exampleand the present disclosure is not limited thereto.

The process extracting module 130 may determine that the context of thecontents is changed based on a change of the object included in thecontents. The process extracting module 130 may determine that thecontext of the contents is changed when some objects of two or moreobjects recognized to be included in the contents are changed. Theprocess extracting module 130 may determine whether the context of thecontents is changed by recognizing two or more objects included in thecontents. The process extracting module 130 may determine whetherobjects corresponding to a predetermined ratio number among two or moreobjects included in the contents are changed, whether objects of apredetermined number are changed, or whether objects of a predeterminedweight or more occupied by a screen are changed. The process extractingmodule 130 recognizes an object included in a first viewpoint and anobject included in a second viewpoint and compares the first viewpointand the second viewpoint to determine whether the context of thecontents is changed by identifying how many objects are changed. Forexample, even when only one object is changed, if a weight occupied bythe object on the screen is large, the process extracting module 130 maydetermine that the context is changed. Alternatively, even when two ormore objects are changed, if a weight occupied by two or more objects onthe screen is small, the process extracting module 130 may determinethat the context is not changed. The detailed description of the contextchange is just an example and the present disclosure is not limitedthereto.

The process extracting module 130 may separate the contents into two ormore subcontents based on the point at which the context is changed. Forexample, when an object 912 included in a content 910 at the firstviewpoint is a cream and an object 922 included in a content 920 at thesecond viewpoint is the blush, the process extracting module 130 checksthat the object is changed to separate the contents into the firstsubcontents 910 which are a step of applying the cream and secondsubcontents 920 which are a step of performing blush makeup. Thedetailed description of the subcontents is just an example and thepresent disclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the point at which thecontext of the contents is changed by using the video processingalgorithm.

The process extracting module 130 may analyze the voice included in thecontents by using the voice analyzing algorithm. The voice included inthe contents may include the voice of the content producer, the voice ofa content photographer, and the like, but this is merely an example, andthe present disclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, thevoice processing algorithm may convert the voice included in thecontents into the image and calculate the image by using a modelincluding the Convolutional Neural Network. When there is a change inpitch or tone of the voice by a predetermined value or more, it may bedetermined that the context of the contents is changed. The concretedescription of the voice processing is just an example and the presentdisclosure is not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine that the context of thecontents is changed when contexts of two or more keywords included inthe voice are changed. For example, if the voice included in the content910 at the first viewpoint is a keyword related to the cream and basicmakeup, and the voice included in the content 920 at the secondviewpoint is a keyword related to the blush and color makeup, theprocess extracting module 130 may determine that the context of thecontents is changed. The detailed description of the context change isjust an example and the present disclosure is not limited thereto.

The process extracting module 130 may separate the contents into two ormore subcontents based on the point at which the context is changed.

According to an exemplary embodiment of the present disclosure, theprocess extracting module 130 may determine the point at which thecontext of the contents is changed by using a natural languageprocessing algorithm.

The process extracting module 130 may analyze the text included in thecontents by using the natural language analyzing algorithm. When thecontents are the video, the text included in the contents may be a textinserted by the content producer into the contents when the contentproducer produces the contents or a text automatically converted intothe caption by the voice included in the contents. The detaileddescription of the text is just an example and the present disclosure isnot limited thereto.

The process extracting module 130 may separate the contents into two ormore subcontents based on the point at which the context of the contentsis changed when the context of the text included in the contents ischanged. The process extracting module 130 may determine that thecontext of the text included in the video is changed and separatecontents for a first point and a second point, respectively into thesubcontents, for example, when the text included in the first point ofthe video is “car repairing” and the text included in the second pointis “car scrapping”. The detailed description of the subcontents is justan example and the present disclosure is not limited thereto.

The process processing module 150 may tag additional information on therespective processes with respect to the extracted process. The processprocessing module 150 may tag the additional information to an objectincluded in the video corresponding to the extracted process. Theadditional information may be additional information related to theprocess. An operation of tagging the additional information may be anoperation of matching and storing the additional information with atleast one of the objects included in the process or the video. Theadditional information may be information provided to the usercorresponding to an operation for receiving a user input, for aninterface object for requesting the additional information. Theadditional information may be provided to the user via, for example, ahyperlink. The hyperlink may be an icon, an image, text, etc., whichallows moving to another part of a current page (or video) or moving toanother page (or another video) based on the input of the user. Theprocess processing module 150 may provide the additional information tothe user when receiving the input of the user for the interface objectfor requesting the additional information of the object included in theprocess or video. The additional information may include at least one ofa URL link, a contact, a map, and an address associated with theprocess. The URL link may be a link including the additional informationon the process. Further, the URL link may be a link for URL that sellsitems or services associated with the process. Further, the URL link maybe a link for connection to another video related to a product or aservice related to the process. For example, in the case of the answerfor the question associated with the purchasing of a used car, theprocess processing module 150 may tag a download address of an SK Encarmobile app, an advertisement video link about SK Encar or the URL linkof Bobaedream with respect to the “used car searching step” process ortag the additional information including the address of a used carcomplex, and the like with respect to the “used car complex visitingstep” process. Further, the process processing module 150 may tag theURL link of a website that sells car insurance with respect to, forexample, an “insurance applying step in purchasing a used car” process.The additional information described above is just an example and theprocess processing module 150 may tag predetermined additionalinformation. Further, the server 100 may store the collected questionsand answers, the category of the answer, the object tagged to theanswer, the process, and the tagged additional information in a storagespace (not illustrated).

According to an exemplary embodiment of the present disclosure, theprocess processing module 150 may match and store the additionalinformation with the subcontents.

The additional information may be additional information provided to theuser with respect to the subcontents.

According to an exemplary embodiment of the present disclosure, theadditional information may be information provided in response to theuser input when receiving the user input relating to the subcontentinterface object from the user terminal. Hereinafter, the additionalinformation will be described below with reference to FIG. 10. FIG. 10illustrates an example of additional information according to anexemplary embodiment of the present disclosure.

The process processing module 150 may provide additional informationwhen receiving the user input relating to the subcontent interfaceobject included in the subcontents 920 from the user terminal. A humanhand shape illustrated in FIG. 10 is auxiliarily illustrated for thedescription and does not limit the interpretation of the presentdisclosure. For example, the process processing module 150 may receivethe user input for the subcontent interface object from the userterminals of users who desire additional information for the blush inassociation with the blush which is an object 922 included in thesubcontents 920. For example, the subcontent interface object may be inthe form of the hyperlink. The process processing module 150 may provideother contents connected to the hyperlink in response to receiving theselection input for the hyperlink of the user terminal. The detaileddescription of the subcontent interface object is just an example andthe present disclosure is not limited thereto.

The additional information may include other new contents. Theadditional information may be various types of contents for providinginformation related to the object included in the subcontents. Forexample, the additional information may be another video, a web site, amap, a contact, an address, a description, and the like, but this ismerely an example and the present disclosure is not limited thereto.

The process processing module 150 may provide additional information1012 in the form of a pop-up within subcontents 1010 according to anexemplary embodiment of the present disclosure. For example, the processprocessing module 150 may transmit additional information 1012 includinga brand and an item number of the blush, as additional information 1012for the blush to the user terminal so as to be displayed on the screenin the form of the pop-up. The detailed description of the additionalinformation is just an example and the present disclosure is not limitedthereto.

The process processing module 150 may provide the additional information1020 to the user terminal in a separate window from the subcontent 920according to an exemplary embodiment of the present disclosure. Forexample, when receiving the user input relating to the subcontentinterface object for the blush object, the process processing module 150may provide to the user terminal the website or the like where the blushmay be purchased. For example, the additional information may be videos,photos, etc., related to the subcontents. The detailed description ofthe additional information is just an example and the present disclosureis not limited thereto.

According to an exemplary embodiment of the present disclosure, theprocess processing module 150 may match and store at least one of acategory of the subcontents or an object related to the subcontents,which is the additional information, with the subcontents.

Hereinafter, a search method will be described. The search method isdescribed with reference to FIG. 12. FIG. 12 illustrates an example of asearch method according to an exemplary embodiment of the presentdisclosure.

The knowledge sharing platform may provide a search interface object1410 to the user terminal so that users may search for desired contents.The users input search information into the search interface object 1410to obtain desired contents, subcontents, or additional informationrelated to the subcontents.

Referring to step (a) of FIG. 12, a storage space 1420 may store aplurality of subcontents and additional information matched with thesubcontents. For example, each of two or more subcontents stored in thestorage space 1420 may be stored with matching categories and objects asadditional information. For example, the category for first subcontents1422 stored in the storage space 1420 may be cosmetic and the object maybe cream, the category for second subcontents 1424 may be cosmetic andthe object is blush, and the category for subcontents 1426 may be carreview and the object may be Ford F150.

Referring to step (b) of FIG. 12, the communication module 170 mayreceive search information from a user terminal (not illustrated). Thecommunication module 170 may receive search information including atleast one of the category and the object from the user terminal. Thecommunication module 170 may receive search information that is the userinput for the search interface object 1410 displayed on the userterminal. The communication module 170 may receive category informationand the object from the user terminal. The search information mayinclude the category information and the object. For example, asdescribed in the example, the communication module 170 may receive thecategory information such as “purchase” and the object such as “usedcar” from the user terminal. For example, the communication module 170may receive category information such as “cosmetic” and an object suchas “blush” from the user terminal. The user terminal may include apersonal computer (PC), a notebook, a mobile terminal, a smart phone, atablet PC, and the like and includes all kinds of terminals which mayaccess a wired/wireless network.

The communication module may include a wired/wireless Internet modulefor accessing the network. As wireless Internet technology, Wireless LAN(WLAN) (Wi-Fi), Wireless broadband (Wibro), World Interoperability forMicrowave Access (Wimax), High Speed Downlink Packet Access (HSDPA), andthe like may be used. As wired Internet technology, X Digital SubscriberLine (XDSL), Fibers to the home (FTTH), power line communication (PLC),and the like may be used.

The communication module 170 may provide at least one of the answer, theprocess, and the tagged additional information corresponding to thesearch information to the user terminal. For example, the communicationmodule 170 may provide at least one of the answer, the process, and thetagged additional information associated with the used car purchasing asdescribed in the example. The communication module 170 may provide atleast one of the answer, the process, and the tagged additionalinformation to the user terminal by using the wired/wireless network.

Referring to step (c) of FIG. 12, the communication module 170 mayprovide at least one of the contents, the subcontents, or the matchedadditional information corresponding to the search information to theuser terminal. The server 100 may determine the subcontents 1424corresponding to the search information among two or more subcontentsstored in the storage space 1420. The communication module 170 maytransmit the subcontents 1424 corresponding to the search information tothe user terminal.

The server 100 compares at least one of the category and the objectincluded in the search information with the category or the objectmatched to each of two or more subcontents stored in the storage spaceto determine at least one of subcontents having a high matching rate orcontents based on the subcontents having the high matching rate. Thecommunication module 170 may transmit the subcontents having the highmatching rate or the contents based on the subcontents to the userterminal. Since the category included in the search information receivedfrom the user terminal is cosmetic and the object is blush, the server100 may determine the second subcontents 1424 having a highest matchingrate for the category and the object among the first subcontents 1422,the second subcontents 1424, and the third subcontents 1426 as a searchresult. Alternatively, the server 100 may determine to transmit thecontents including the second subcontents 1424 or the additionalinformation stored with matching the second subcontents 1424, to theuser terminal. The communication module 170 may transmit at least one ofthe second subcontents 1424, the contents including the secondsubcontents 1424, and the additional information stored with matchingthe second subcontents 1424, to the user terminal. The detaileddescription of the search is just an example and the present disclosureis not limited thereto.

The server 100 may determine to transmit at least one of contents havingreliability equal to or higher than a predetermined value, subcontentsbased on the contents, and additional information, to the user terminal.The communication module 170 may transmit at least one of contentshaving reliability equal to or higher than a predetermined value andsubcontents, to the user terminal. The communication module 170 maytransmit subcontents based on the contents having reliability equal toor higher than a predetermined value, to the user terminal.

According to an exemplary embodiment of the present disclosure, userscan perform a search that includes two or more depths. The knowledgesharing platform may provide a search interface object corresponding totwo or more depths so that the users may search for desired contents.

The communication module 170 may receive first depth search informationfrom the user terminal. The communication module 170 may receive thefirst depth search information including at least one of the categoryand the object from the user terminal. The server 100 compares the firstdepth search information with two or more contents, sub contents, ormatched additional information stored in the storage space to determinethe contents, the sub contents, or the matched additional informationcorresponding to the first depth search information. The communicationmodule 170 may provide at least one of the contents, the subcontents,and the matched additional information corresponding to the first depthsearch information, to the user terminal.

The communication module 170 may receive second depth search informationfor the first depth search information, from the user terminal. Theusers may perform the search again among results according to the firstdepth search information. The communication module 170 may receive thesecond depth search information including at least one of the categoryand the object. The server 100 may determine at least one of contents,subcontents, or matched additional information corresponding to thesecond depth search information, from the contents, the subcontents, orthe matched additional information corresponding to the first depthsearch information. The communication module 170 may transmit at leastone of the contents, the subcontents, and the matched additionalinformation corresponding to the second depth search information, to theuser terminal. That is, the users may perform the search once again fromthe search result.

For example, the communication module 170 may receive the first depthsearch information including the category from the user terminal. Thecommunication module 170 may receive the first depth search informationincluding “cosmetic” as the category from the user terminal. The server100 compares two or more subcontents stored in the storage space withthe first depth search information to determine at least one ofsubcontents having a high matching rate, contents based on thesubcontents, and additional information. The communication module 170may transmit a result corresponding to the first depth searchinformation to the user terminal. The user may perform a second depthsearch in order to view the search result corresponding to “blush” inthe “cosmetic” search result. The communication module 170 may receivethe second depth search information including “blush” as the object fromthe user terminal. The server 100 may determine at least one ofsubcontents having a high matching rate with “blush” in the “cosmetic”search result, contents based on the subcontents, and additionalinformation. The communication module 170 may transmit a search resultcorresponding to “blush” in the “cosmetic” search result, to the userterminal. The detailed description of the search information is just anexample and the present disclosure is not limited thereto.

Hereinafter, a method for displaying the contents on the user terminalwill be described. The method for displaying the contents on the userterminal will be described with reference to FIG. 13. FIG. 13illustrates an example of contents and subcontents according to anexemplary embodiment of the present disclosure.

The knowledge sharing platform may provide the contents to the users.The knowledge sharing platform transmits information on the contents tothe user terminal to display the contents on the screen of the userterminal.

When the communication module 170 receives a selection input for one oftwo or more contents from the user terminal, the server 100 maydetermine to transmit information for displaying the contents to theuser terminal. A screen 1500 illustrated in FIG. 13 shows an example inwhich the user views the video through the knowledge sharing platform.The screen displayed on the user terminal illustrated in FIG. 13 is justan example and the present disclosure is not limited thereto. Forexample, the communication module 170 may receive a selection input for“10 Easy Steps” contents 1510 of two or more contents from the userterminal. The server 100 may determine to display the contents 1510 onthe user terminal. The detailed description of the contents is just anexample and the present disclosure is not limited thereto.

The communication module 170 may transmit data for displaying thecontents and two or more subcontents based on the contents, to the userterminal. Two or more subcontents based on the contents may be dataprovided based on the input of the user or data basically provided evenif there is no input from the user. According to an exemplary embodimentof the present disclosure, when communication module 170 receives theselection input for the contents from the user terminal, the server 100determines to display the contents and two or more subcontents based onthe contents on the user terminal. According to another exemplaryembodiment of the present disclosure, when the communication module 170receives the selection input for the contents from the user terminal,the server 100 allows an interface object which allows the contents tobe displayed on the user terminal and the subcontents to be additionallyviewed to be displayed on the user terminal. Users who want to furtherview the subcontents based on the contents may transmit to the knowledgesharing platform the selection input for the interface object whichallows the subcontents to be additionally viewed. When the communicationmodule 170 receives the selection input for the interface object whichallows the subcontents to be additionally viewed from the user terminal,the server 100 may allow two or more subcontents based on the contentsto be displayed on the user terminal.

The communication module 170 may transmit data for displaying two ormore subcontents stored in the storage space for the contents on theuser terminal. The communication module 170 may transmit data fordisplaying two or more subcontents stored on the user terminal whensubcontents acquired by separating the contents into two or moresubcontents are stored in the storage space. The communication module170 may transmit data for displaying additional information stored inthe storage space, which is matched with the subcontents on the userterminal together.

The server 100 may determine to display different types of additionalinformation on the user terminal based on the reliability of theproducer of the contents.

A memory may store additional information corresponding to two or moretypes. Basic additional information may include information that isirrelevant to a monetary interest of additional information related tothe process. Commerce additional information may include informationthat is relevant to the monetary interest of the additional informationrelated to the process. The basic additional information may include,for example, a subtitle of the subcontents, another video irrelevant tothe monetary interest, a website address, and the like. The commerceadditional information may be, for example, a homepage URL forpurchasing the object included in the subcontents, which is relevant tothe momentary interest, a promotional phrase of a webpage, which isrelevant to the subcontents, etc. Further, for example, the commerceadditional information may include an advertisement. The detaileddescription of the additional information is just an example and thepresent disclosure is not limited thereto.

The process extracting module 130 may determine the reliability for theproducer for the contents to be displayed on the user terminal. Asdescribed above, the reliability for the contents may be determinedbased on the feedback of the user.

According to an exemplary embodiment of the present disclosure, theserver 100 may determine to display basic additional information matchedwith the subcontents on the user terminal when the reliability of theproducer of the contents is lower than a predetermined threshold. Thecommunication module 170 may transmit data which allows the basicadditional information to be displayed on the user terminal. The server100 may transmit data for displaying at least one of the basicadditional information and the commerce additional information matchedwith the subcontents on the user terminal when the reliability of thecontents for the producer is equal to or higher than a predeterminedthreshold. The commerce additional information may be provided to theuser only for the contents for contents producers having reliabilityequal to or higher than a predetermined threshold. The detaileddescription of the contents is just an example and the presentdisclosure is not limited thereto.

According to another exemplary embodiment of the present disclosure,when the reliability of the contents producer is lower than apredetermined first threshold, the server 100 may not provide theadditional information for the contents or the subcontents for thecorresponding contents producer to the user terminal. The server 100 maydetermine to display the basic additional information on the userterminal for the contents of the corresponding contents producer whenthe reliability for the contents producer is equal to or higher than thepredetermined first threshold and lower than a predetermined secondthreshold. The communication module 170 may transmit data which allowsthe basic additional information to be displayed on the user terminal.The server 100 may determine to display, on the user terminal, at leastone of the basic additional information and the commerce additionalinformation for the contents of the corresponding contents producer whenthe reliability of the contents producer is equal to or higher than thepredetermined second threshold. The communication module 170 maytransmit data which allows at least one of the basic additionalinformation and the commerce additional information to be displayed onthe user terminal.

According to an exemplary embodiment of the present disclosure, thebasic additional information may be information displayed together withthe subcontents on the user terminal, even when there is no separateselection input of the user. For example, the server 100 may allow thebasic additional information to be displayed on one side of thesubcontents or one side other than the subcontents in the user terminal.The commerce additional information may be information displayedtogether with the subcontents on the user terminal, even when there is aseparate selection input of the user. For example, even in the case ofthe subcontents for the contents producer having the reliability equalto or higher than the predetermined second threshold, only the basicadditional information may be provided to a basic screen. In addition,when the communication module 170 receives the selection input for auser interface object for requesting the commerce additional informationfrom the user terminal, the server 100 may determine to additionallydisplay the commerce additional information on the user terminal. Thecommunication module 170 may transmit to the user terminal informationfor displaying the commerce additional information on the user terminal.The detailed description of the additional information is just anexample and the present disclosure is not limited thereto.

The server 100 may allow the contents to be displayed on one side of thescreen 1500 displayed on the user terminal and two or more subcontentsbased on the contents to be displayed on the other side. For example,the “10 Easy Steps” contents 1510 are separated to store firstsubcontents 1522 of a basic makeup step, second subcontents 1524 of aconcealer makeup step, and third subcontents 1526 of a color makeup stepin the storage space. The server 100 may determine to display thesubcontents and the stored additional information matched with thesubcontents on the user terminal. The communication module 170 maytransmit data which allows the subcontents or the additional informationto be displayed on the user terminal. The detailed description of thesubcontents is just an example and the present disclosure is not limitedthereto.

The communication module 170 may receive the selection input for one ofthe two or more subcontents from the user terminal. The server 100 maydetermine to display the one subcontent and the additional in formationstored with matching the one subcontent on the user terminal instead ofthe contents. For example, as illustrated in FIG. 13, when the userdesires to view the third subcontents of the first subcontents, thesecond subcontents, and the third subcontents acquired by separating thecontents, the user may transmit data by clicking on or touching with ahand a display object corresponding to the third subcontents. The shapeof the human hand illustrated in FIG. 13 is just an auxiliary means fordescribing the disclosure and the present disclosure is not limitedthereto. When the communication module 170 receives the selection inputfor the third subcontents on the screen 1500, the server 100 maydetermine to display the video corresponding to the third subcontents,the third subcontents, and the additional information stored withmatching the third subcontents on the user terminal instead of thecontents. For example, when the user clicks on the third subcontentswhile watching the video corresponding to 00 minute and 43 seconds ofthe contents, since the video corresponding to the third subcontents is3 minutes 50 seconds to 4 minutes 03 seconds, the video from 3 minutesand 50 seconds may be displayed on the user terminal. The detaileddescription of the method for displaying the contents, the subcontents,and the additional information on the user terminal is just an exampleand the present disclosure is not limited thereto.

FIG. 2 is a flowchart of a method for providing knowledge basede-commerce according to an exemplary embodiment of the presentdisclosure.

The server 100 may collect the question and the answer for the questionfrom the knowledge sharing platform (210). The knowledge sharingplatform may be a platform of a type in which the users arbitrarily askquestions and answerers arbitrarily answers the questions. Further,according to an exemplary embodiment of the present disclosure, theknowledge sharing platform may be a platform for uploading the videoincluding the question or the answer. Further, in the exemplaryembodiment of the present disclosure, the knowledge sharing platform maybe a knowledge sharing platform in which the answer is input to beseparated for each process. Further, as described above, the questionsand the answers according to the exemplary embodiment of the presentdisclosure may include at least contents associated with e-commerce.

The server 100 may extract the process for solving the question from theanswer by analyzing the collected answer (230). Further, the server 100may determine the category of at least one of the collected question andanswer and tag the object to at least one of the question and theanswer. The server 100 may determine at least one category of thequestion and the answer included in the collected video and tag theobject to at least one of the question and the answer.

For example, in the case of the question associated with the used carpurchasing, the category of the question may be determined as “purchase”and the object may be “used car”. In more detail, for the question “Whatis the present price of a used car such as an AVANTE XD 2006? The AVANTEXD 2006 is scheduled to be purchased”, the server 100 may determine thecategory of the question as “purchase”. Further, the server 100 may tagthe object such as “used car” to the question. For example, as describedabove, in the case of a question associated with used car purchasing,the category of the answer may be determined as “purchase” and theobject may be “used car”. Further, the server 100 may extract theprocess of “used car purchasing” by analyzing the answer. The extractedprocess may be processes including “used car searching step”, “used carcomplex visiting step”, “vehicle checking step before the contract”,“contract step”, “transfer step”, and the like. The detailed descriptionof the process separation is just an example and the present disclosureis not limited thereto.

For example, in the case of a video related to a question and an answerrelated to the purchase of the used car, the server 100 may determinethe category of the question as “purchase” and determine the object as“used car”. The server 100 analyzes the answer included in the video toextract the process of “purchase of used car”. For example, the server100 may separate the answer included in the video into processes such as“used car search step” from 0 minute and 34 seconds to 1 minute and 02seconds and “vehicle checking step before contract” from 1 minute and 03seconds to 1 minute and 37 seconds. Further, for example, the server 100analyzes the answers included in the video to separate the video of apart where the object for the search window is identified into theprocess such as “used car search step” and the video of a part where anobject for an operation of picking up the vehicle with a camera isidentified into the process such as “vehicle checking step beforecontract”. The detailed description of the process separation is just anexample and the present disclosure is not limited thereto.

The server 100 may tag the additional information on the respectiveprocesses to the extracted processes and store the tagged additionalinformation (250). The server 100 may tag the additional information onthe object included in the video corresponding to the process. Asdescribed above, the additional information may include at least one ofa URL link, a contact, a map, and an address associated with theprocess.

The tagged additional information may be determined based on thereliability of the contents or the contents producer. For example, thebasic additional information may be tagged when the reliability of thecontents or the contents producer is equal to or lower than apredetermined value. The basic additional information may includeinformation that is irrelevant to the monetary interest of additionalinformation related to the process. For example, the commerce additionalinformation may be tagged when the reliability of the contents or thecontents producer is higher the predetermined value. The commerceadditional information may include information that is relevant to themonetary interest of the additional information related to the process.The basic additional information may include, for example, a subtitle ofthe subcontents, another video irrelevant to the monetary interest, awebsite address, and the like. The commerce additional information maybe, for example, a homepage URL for purchasing the object included inthe subcontents, which is relevant to the momentary interest, apromotional phrase of a webpage, which is relevant to the subcontents,etc. Further, for example, the commerce additional information mayinclude an advertisement. The detailed description of the additionalinformation is just an example and the present disclosure is not limitedthereto.

For example, as described above, in the case of the question associatedwith the used car purchasing, the server 100 may tag the additionalinformation such as the download links of the SK Encar mobile app, anadvertisement video link for SK Encar, or the like with respect to theprocess of “used car searching step”. For example, the server 100 maytag the additional information by inserting an advertisement videohyperlink related to the SK Encar into a video part corresponding to theprocess of “used car search step”. Alternatively, for example, in thecase of a question concerning the cosmetics, the server 100 clicks onthe “blush” object included in the video in the video part correspondingto the process of “blush makeup step” to tag additional information tomove to a site where the blush may be purchased.

The server 100 may receive the search information from the user terminal(270). To this end, the server 100 may include the wired/wirelessInternet module for accessing the network. Further, the server 100 mayreceive the category information and the object from the user terminal.The server 100 may receive the search information to search the answer,the process, and the tagged additional information from the userterminal.

The server 100 may provide at least one of the answer, the process, andthe tagged additional information corresponding to the searchinformation to the user terminal (290). The server 100 may provide atleast one of the answer, the process, and the tagged additionalinformation corresponding to the search information to the user terminalthrough the network. For example, the server 100 may provide at leastone of one part of the video including the answer corresponding to thesearch information, at least one object included in the videocorresponding to the answer, and additional information tagged withrespect to the video to the user terminal.

FIG. 3 illustrates an example of an analysis data table of a questionand an answer which are collected according to an exemplary embodimentof the present disclosure.

An analysis data table of the collected question and answer according tothe exemplary embodiment of the present disclosure may include acategory 310, an object 320, a process 330, and detailed contents 340 ofthe process in the answer.

The category 310 of the answer may be a category associated with a typeof wants of the questioner and the answerer. As described above, thequestions and the answers according to the present disclosure mayinclude at least contents associated with e-commerce. The type of thewant of the questioner or answerer associated with e-commerce may beassociated with purchasing, selling and manufacturing of items orpurchasing and selling of services. Therefore, the category 310 mayclassify the type of the want of the questioner or the answerer into“want to purchase”, “want to sell”, “want to make”, and the like.“Target” may be present in the want of the questioner or answerer andthe target of the want may become the object 320. The answerer mayanswer a process for satisfying the want of the questioner and such aprocess may become the process 330. The process 330 may include thedetailed contents 340 of the process for each process. The detailedcontents 340 of the process may include contents to separate the answerprepared by the answerer for each process.

In the example illustrated in FIG. 3, when the question is “I want topurchase the used car” and the answer is contents associated with theprocess of purchasing the used car, the want of the user is “want topurchase” in the answer, therefore, the category of the answer maybecome “purchase”. Further, the answer is contents associated with theprocess of purchasing the “used car” and the object 320 of the want suchas the “purchase” may become the “used car”. The answerer may answer theprocess of purchasing the used car and in the example illustrated inFIG. 3, as the process of purchasing the used car, “contract preparingstep”, “insurance applying step”, and “vehicle registration applyingstep” may become the process 330 of purchasing the used car.Alternatively, in another example, when the server 100 collects thevideos for the question and the answer in the knowledge sharingplatform, the server 100 may perform the analysis for the question andthe answer based on the videos. The server 100 may extract the process330 described in the example by analyzing the answer.

FIG. 4 illustrates an example of analysis of an answer according to anexemplary embodiment of the present disclosure.

FIG. 4 illustrates a more detailed example of analyzing an answer andseparating the answer into processes according to an exemplaryembodiment of the present disclosure.

FIG. 4 illustrates an example of analyzing the answer for the questionsuch as “I want to purchase the used car” as illustrated in FIG. 3. Asillustrated in FIG. 4, The answerer may answer the process of purchasingthe used car for the question such as “I want to purchase the used car”and in the example illustrated in FIG. 4, as the process of purchasingthe used car, the “contract preparing step”, the “insurance applyingstep”, and the “vehicle registration applying step” may become theprocess 330 of purchasing the used car. The detailed contents 340 of theprocess may include contents to separate the answer contents prepared bythe answerer for each process described above. Further, the server 100may tag the additional information 350 with respect to the respectiveprocesses. As described above, the additional information 350 on theprocess may include at least one of the URL link, the contact, the map,and the address associated with the process.

In the example of FIG. 4, the detailed contents 340 of the process,which may include contents including cautions while preparing thecontract such as “while preparing the contract” prepared by theanswerer, are present in the “contract preparing” process 330. Thedetailed contents 340 of the process for the process 330 such as“preparing the contract” may be a video including contents includingcautions while preparing the contract. The server 100 may tag theadditional information 350 for each separated process 330 and additionalinformation to the “contract preparing” process 330 may become adownload link of “contract form” or a link for accessing the videorelated to the contract preparing. The process, the detailed contents,and the additional information described above are just examples and thepresent disclosure may include a predetermined process, predeterminedprocess detailed information, and predetermined additional information.

FIG. 5 illustrates a more detailed example of analysis of an answeraccording to an exemplary embodiment of the present disclosure.

FIG. 5 illustrates the answer, the keyword, and the detailed contents ofthe process. In FIG. 5, the text following “Hi! This is *** a used carused car salesman.” corresponds to the answer. The server 100 mayanalyze the answer. Recommending the answer may be a feedback for theanswer. The server 100 may collect the answer, extract the keyword fromthe answer, and extract the process for solving the question. In theFIG. 5, “If your car is not a private sale, you should go to a used cardealing complex. When you go to the dealing complex, you should firstprepare a contract after checking the interior and the exterior of avehicle, sufficiently performing test-drive, and accurately receiving anotification of whether an accident occurs in the corresponding vehicleand a vehicle performance record. If you pay a vehicle payment, anacquisition/registration tax, and a used car dealing fee (selling fee, acommission fee, and the like), the used car dealing complex willdeputize vehicle transfer. In association with insurance, a militarydriving carrier does not apply to you and you will be regarded as newapplication. When you apply self-vehicle insurance, an insurance premiumwill be approximately 2 million won.” corresponds to the answer. Herein,“vehicular interior/exterior 331”, “test drive 332”, “insuranceassociated matters 333”, and “knack or cautions 334” may correspond tothe keyword. The server 100 analyzes the answer to divide the answerinto the process 330 such as “step of checking the vehicularinterior/exterior”, “test drive step”, “insurance applying step”, and“other cautions step”.

In the answer of FIG. 5, “Since the vehicle is expensive, you shouldcheck the vehicle with your eyes, sufficiently perform the test drive,and receive the notification of the vehicle performance record andcarefully check what an accident range is or whether the vehicle iscompletely repaired if the accident occurs in the vehicle. I hope you topurchase a best car. (341)” as the answer may correspond to the detailedcontents 340 of the process of the “other cautions step”.

FIG. 6 illustrates an example of additional information of an analyzedanswer according to an exemplary embodiment of the present disclosure.

FIG. 6 illustrates an example of additional information tagged for eachprocess of the answer analyzed in FIG. 5. In FIG. 5, the server 100 maydivide the answer into the process 330 such as the “step of checking thevehicular interior/exterior”, the “test drive step”, the “insuranceapplying step”, and the “other cautions step”.

In this case, the server 100 may tag a link of a blog web documentassociated with “a part to be primarily checked in the interior/exteriorof the vehicle” in a special blog for the vehicle or a link foraccessing a video for describing a part to be primarily checked in theexterior of the vehicle, with respect to the “step of checking thevehicular interior/exterior”. Therefore, the user may easily obtaindetailed information including “information on a place which becomesrusty on the bottom of a vehicle body, and the like”, “method forfinding a trace of the accident”, and the like in the “step of checkingthe vehicular interior/exterior”. For example, when the answer isdescribed only by a word, the additional information is transferred tothe user through a photograph or the video to allow the user to cleverlyand conveniently perform a desired dealing action by betterunderstanding answer contents.

The server 100 may tag a link of “a test-drive review” document loadedin an automobile magazine or a link for accessing a video acquired byphotographing the test-driver review of the car with respect to the“step of perform the test drive”. Therefore, the user may more easilyobtain detailed information regarding check points while test-drivingthe vehicle, and the like.

The server 100 tags the URL link of a fire insurance homepage or a linkfor accessing an insurance applying related promotional video, withrespect to the “step of applying for insurance, and the like” to allowthe user to more easily verify insurance information and easily applyfor insurance, thereby accurately and conveniently satisfy the want ofthe user. Additional information associated with a business such as aninsurance company homepage may be tagged to a corresponding company byreceiving a predetermined advertisement fee.

FIG. 7 is a block diagram of a computer which performs an operation ofexecuting a computer program for providing knowledge based e-commerceaccording to an exemplary embodiment of the present disclosure.

Referring to FIG. 7, simple and general description of an appropriatecomputing environment in which various aspects of an exemplaryembodiment according to the present disclosure may be implemented may beprovided.

The present disclosure has generally been described above in associationwith a computer executable command which may be executed on one or morecomputers, but it will be well appreciated by those skilled in the artthat the present disclosure can be implemented through a combinationwith other program modules and/or a combination of hardware andsoftware.

In general, the program module includes a routine, a program, acomponent, a data structure, and the like that execute a specific taskor implement a specific abstract data type. Further, it will be wellappreciated by those skilled in the art that the method of the presentdisclosure can be implemented by other computer system configurationsincluding a personal computer, a handheld computing device,microprocessor-based or programmable home appliances, and others (therespective devices may operate in connection with one or more associateddevices)) as well as a single-processor or multi-processor computersystem, a mini computer, and a main frame computer.

The aspects described in the present disclosure may also be implementedin a distributed computing environment in which predetermined tasks areperformed by remote processing devices connected through a communicationnetwork. In the distributed computing environment, the program modulemay be positioned in both local and remote memory storage devices.

The computer generally includes various computer readable media. Mediaaccessible by the computer may be computer readable media regardless oftypes thereof and the computer readable media include volatile andnon-volatile media, and mobile and non-mobile media. As not a limit butan example, the computer readable medium may include both a computerstorage medium and a communication medium. The computer storage mediumincludes all of the volatile and non-volatile and the mobile andnon-mobile media implemented by a predetermined method or technology forstoring information such as a computer readable command, a datastructure, a program module, or other data. The computer storage mediainclude a RAM, a ROM, an EEPROM, a flash memory or other memorytechnologies, a CD-ROM, a digital video disk (DVD) or other optical diskstorage devices, a magnetic cassette, a magnetic tape, a magnetic diskstorage device or other magnetic storage devices or predetermined othermedia which may be accessed by the computer or may be used to storedesired information, but are not limited thereto.

The communication media generally implement the computer readablecommand, the data structure, the program module, or other data in acarrier wave or a modulated data signal such as other transportmechanism and include all information transfer media. The term modulateddata signal means a signal acquired by configuring or changing one ormore of characteristics of the signal so as to encode information in thesignal. As not a limit but an example, the communication media includewired media such as a wired network or a direct-wired connection andwireless media such as acoustic, RF, infrared and other wireless media.A combination of any media among the aforementioned media is alsoincluded in the range of the computer readable media.

An exemplary environment 1100 that implements various aspects of thepresent disclosure including a computer 1102 is shown and the computer1102 includes a processing device 1104, a system memory 1106, and asystem bus 1108. The system bus 1108 connects system componentsincluding the system memory 1106 (not limited thereto) to the processingdevice 1104. The processing device 1104 may be a predetermined processoramong various commercial processors. A dual processor and othermulti-processor architectures may also be used as the processing device1104.

The system bus 1108 may be any one of several types of bus structureswhich may be additionally interconnected to a local bus using any one ofa memory bus, a peripheral device bus, and various commercial busarchitectures. The system memory 1106 includes a read only memory (ROM)1110 and a random access memory (RAM) 1112. A basic input/output system(BIOS) is stored in the non-volatile memories 1110 including the ROM,the EPROM, the EEPROM, and the like and the BIOS includes a basicroutine that assists in transmitting information among components in thecomputer 1102 at a time such as in-starting. The RAM 1112 may alsoinclude a high-speed RAM including a static RAM for caching data, andthe like.

The computer 1102 also includes an embedded hard disk drive (HDD) 1114(for example, EIDE and SATA)—the embedded hard disk drive (HDD) 1114 mayalso be configured for an exterior purpose in an appropriate chassis(not illustrated)—, a magnetic floppy disk drive (FDD) 1116 (forexample, for reading from or writing in a mobile diskette 1118), and anoptical disk drive 1120 (for example, for reading a CD-ROM disk 1122 orreading from or writing in other high-capacity optical media such as theDVD, and the like). The hard disk drive 1114, the magnetic disk drive1116, and the optical disk drive 1120 may be connected to the system bus1108 by a hard disk drive interface 1124, a magnetic disk driveinterface 1126, and an optical drive interface 1128, respectively. Aninterface 1124 for implementing an exterior drive includes at least oneof a universal serial bus (USB) and an IEEE 1394 interface technology orboth of them.

The drives and the computer readable media associated therewith providenon-volatile storage of the data, the data structure, the computerexecutable command, and others. In the case of the computer 1102, thedrives and the media correspond to storing predetermined data in anappropriate digital format. In the description of the computer readablemedia, the mobile optical media such as the HDD, the mobile magneticdisk, and the CD or the DVD are mentioned, but it will be wellappreciated by those skilled in the art that other types of mediareadable by the computer such as a zip drive, a magnetic cassette, aflash memory card, a cartridge, and others may also be used in anexemplary operating environment and further, the predetermined media mayinclude computer executable commands for executing the methods of thepresent disclosure.

Multiple program modules including an operating system 1130, one or moreapplication programs 1132, other program module 1134, and program data1136 may be stored in the drive and the RAM 1112. All or some of theoperating system, the application, the module, and/or the data may alsobe cached by the RAM 1112. It will be well appreciated that the presentdisclosure may be implemented in various operating systems which arecommercially usable or a combination of the operating systems.

A user may input commands and information in the computer 1102 throughone or more wired/wireless input devices, for example, pointing devicessuch as a keyboard 1138 and a mouse 1140. Other input devices (notillustrated) may include a microphone, an IR remote controller, ajoystick, a game pad, a stylus pen, a touch screen, and others. Thedevices and other input devices are often connected to the processingdevice 1104 through an input device interface 1142 connected to thesystem bus 1108, but may be connected by other interfaces including aparallel port, an IEEE 1394 serial port, a game port, a USB port, an IRinterface, and others.

A monitor 1144 or other types of display devices are also connected tothe system bus 1108 through interfaces such as a video adapter 1146, andthe like. In addition to the monitor 1144, the computer generallyincludes a speaker, a printer, and other peripheral output devices (notillustrated).

The computer 1102 may operate in a networked environment by using alogical connection to one or more remote computers including remotecomputer(s) 1148 through wired and/or wireless communication. The remotecomputer(s) 1148 may be a workstation, a server computer, a router, apersonal computer, a portable computer, a micro-processor basedentertainment apparatus, a peer device, or other general network nodesand generally includes multiple components or all of the componentsdescribed with respect to the computer 1102, but only a memory storagedevice 1150 is illustrated for brief description. The illustratedlogical connection includes a wired/wireless connection to a local areanetwork (LAN) 1152 and/or a larger network, for example, a wide areanetwork (WAN) 1154. The LAN and WAN networking environments are generalenvironments in offices and companies and facilitate an enterprise-widecomputer network such as Intranet, and the like and all of them may beconnected to a worldwide computer network, for example the Internet.

When the computer 1102 is used in the LAN networking environment, thecomputer 1102 is connected to a local network 1152 through a wiredand/or wireless communication network interface or an adapter 1156. Theadapter 1156 may facilitate the wired or wireless communication in theLAN 1152 and the LAN 1152 also includes a wireless access pointinstalled therein in order to communicate with the wireless adapter1156. When the computer 1102 is used in the WAN networking environment,the computer 1102 may include a modem 1158 or has other means thatconfigure communication through the WAN 1154 such as connection to acommunication server on the WAN 1154 or connection through the Internet.The modem 1158 which may be an embedded or exterior and wired orwireless device is connected to the system bus 1108 through the serialport interface 1142. In the networked environment, the program modulesdescribed with respect to the computer 1102 or some aspect thereof maybe stored in the remote memory/storage device 1150. The illustratednetwork connection is exemplary and it will be well appreciated thatother means configuring a communication link among computers may beused.

The computer 1102 performs an operation of communicating withpredetermined wireless devices or entities which are disposed andoperated by the wireless communication, for example the printer, ascanner, a desktop and/or portable computer, a portable data assistant(PDA), a communication satellite, predetermined equipment or place (forexample, a kiosk, a newsstand, and a toilet) associated with a wirelessdetectable tag, and a telephone. This at least includes Wi-Fi and aBluetooth™ wireless technology. Accordingly, communication may be apredefined structure like the network in the related art or just ad hoccommunication between at least two devices.

Wireless fidelity (Wi-Fi) enables connection to the Internet, and thelike from a sofa of a home, a bed of a hotel room, or a conference roomof an office without a wired cable. Wi-Fi is a wireless technology suchas a device, for example a cellular phone, which enables the computer totransmit and receive data indoors or outdoors, that is, anywhere in acommunication range of a base station. The Wi-Fi network uses a wirelesstechnology called IEEE 802.11(a, b, g, and others) in order to providesafe, reliable, and high-speed wireless connection. The Wi-Fi may beused to connect the computers to each other or the Internet and thewired network (using IEEE 802.3 or Ethernet). The Wi-Fi network mayoperate, for example, at a data rate of 11 Mbps (802.11a) or 54 Mbps(802.11b) in unlicensed 2.4 and 5 GHz wireless bands or operate in aproduct including both bands (dual bands), therefore, the network mayprovide actual performance similar to a basic 10 BaseT wired Ethernetnetwork used in a lot of offices.

FIG. 8 is a schematic block diagram of an exemplary computingenvironment that executes a computer program for providing knowledgebased e-commerce according to an exemplary embodiment of the presentdisclosure.

Referring to FIG. 8, a system 1200 includes one or more client(s) 1202.The client(s) 1202 may be hardware and/or software (for example, athread, a process, and a computing device). The client(s) 1202 may, forexample, keep a cookie(s) and/or associated situational information byusing the present disclosure.

The system 1200 also includes one or more server(s) 1204. The server(s)1204 may also be the hardware and/or software (for example, the thread,the process, and the computing device). The server 1204 may, forexample, keep the thread which performs conversion by using the presentdisclosure. One available communication between the client 1202 and theserver 1204 may be a form of a data packet configured to be transmittedamong two or more computer processes. The data packet may include, forexample, the cookie(s) and/or the associated situational information.The system 1200 includes a communication framework 1206 (for example, aglobal communication network such as Internet, and the like) which maybe used for facilitating communications between the client(s) 1202 andthe server(s) 1204.

Wired (including an optical fiber) and/or wireless technology mayfacilitate the communication. The client(s) 1202 operate(s) inconnection with one or more client data storage(s) 1208 which may beused for storing information (for example, the cookie(s) and/or theassociated situational information) which is local to the client(s)1202. Similarly to this, the server(s) 1204 operate(s) in connectionwith one or more server data storage(s) 1210 which may be used forstoring information which is local to the servers 1204.

The components include the examples of the present disclosure. Ofcourse, it is not possible to describe all considerable combinations ofcomponents or methods for the purpose of describing the presentdisclosure, but it will be appreciated by those skilled in the art thata lot of additions of the present disclosure can be combined orreplaced. Therefore, the present disclosure is used for embracing all ofthe changes, modifications, and transformations included in the spiritand the scope of the appended claims. Moreover, it is construed that upto a degree in which a term “include” is used in any one of the detaileddescription and the claims, when the term is used as a transitional wordin the claims, the term is interpreted in a similar manner to the term“comprising”.

The present disclosure has been described with reference to thepreferred embodiments. However, it will be appreciated by those skilledin the art that various modifications and changes of the presentdisclosure can be made without departing from the spirit and the scopeof the present disclosure which are defined in the appended claims andtheir equivalents.

What is claimed is:
 1. A non-transitory computer readable medium storinga computer program, wherein when the computer program is executed by oneor more processors of a computing device, the computer program performsoperations for analyzing contents, and the operations include:separating contents into one or more subcontents by analyzing thecontents; matching and storing additional information with thesubcontents; receiving search information from a user terminal; andsending at least one of the contents, the subcontents or the matchedadditional information corresponding to the search information to theuser terminal, wherein the separating contents into one or moresubcontents by analyzing the contents includes: identifying a feedbackinformation about the contents or a producer of the contents;determining reliability of the contents or a producer of the contentsbased on the feedback information; and separating the contents havingthe reliability equal to or higher than a predetermined threshold intoone or more subcontents.
 2. The non-transitory computer readable mediumaccording to claim 1, wherein the contents include at least one of videoor question and answer text.
 3. The non-transitory computer readablemedium according to claim 1, wherein the separating contents into one ormore subcontents by analyzing the contents includes: separating thecontents into two or more subcontents based on a point at which acontext of the contents is changed.
 4. The non-transitory computerreadable medium according to claim 1, wherein the separating contentsinto one or more subcontents by analyzing the contents includes:analyzing a video included in the contents using an image processingalgorithm; determining that a context of the contents has changed basedon a change of an item included in the contents; and separating thecontents into two or more subcontents based on a point at which acontext is changed.
 5. The non-transitory computer readable mediumaccording to claim 1, wherein the separating contents into one or moresubcontents by analyzing the contents includes: analyzing an audiosignal included in the contents using an audio analyzing algorithm;determining that a context of the contents has changed based on a changeof a context of two or more keywords included in the voice; andseparating the contents into two or more subcontents based on a point atwhich a context is changed.
 6. The non-transitory computer readablemedium according to claim 1, wherein the separating contents into one ormore subcontents by analyzing the contents includes: analyzing the textincluded in the contents using a natural language analysis algorithm;and separating the contents into two or more subcontents based on acontext change point of the contents based on a change of a context ofthe text included in the contents.
 7. The non-transitory computerreadable medium according to claim 1, wherein the additional informationis an additional information provided to the user regarding thesubcontents.
 8. The non-transitory computer readable medium according toclaim 1, wherein the additional information is an information that isprovided corresponding to a user input if the user input relating to asubcontents interface object is received from the user terminal.
 9. Thenon-transitory computer readable medium according to claim 1, whereinthe additional information includes at least one of a category of thesubcontents or an object related to the subcontents.
 10. Thenon-transitory computer readable medium according to claim 1, whereinthe feedback information includes at least one of data receiving aselection input for an interface object indicating affirmation or denialof the contents which is received from a user terminal, or a text datadescribing the contents received from the user terminal.
 11. Thenon-transitory computer readable medium according to claim 1, whereinthe receiving search information from a user terminal includes:receiving search information including at least one of a category or anobject, and wherein the sending at least one of the contents, thesubcontents or the matched additional information corresponding to thesearch information to the user terminal includes: comparing at least oneof the category or the object included in the search information with acategory or an object which is matched to each of two or moresubcontents which are stored in the storage; and transmitting at leastone of sub contents having a high matching rate or contents based on thesubcontents having a high matching rate.
 12. The non-transitory computerreadable medium according to claim 1, wherein the sending at least oneof the contents, the subcontents or the matched additional informationcorresponding to the search information to the user terminal includes:transmitting at least one of contents or subcontents having reliabilityequal to or higher than a predetermined threshold.
 13. Thenon-transitory computer readable medium according to claim 1, whereinthe operations further include: transmitting data to the user terminalto display the contents and two or more subcontents based on thecontents.
 14. The non-transitory computer readable medium according toclaim 13, wherein the operations further include: receiving a selectioninput relating to one subcontent of the two or more subcontents from theuser terminal; and transmitting data to display the one subcontent andadditional information which is matched to the one subcontent and storedin the storage to the user terminal.
 15. The non-transitory computerreadable medium according to claim 13, wherein the operations furtherinclude: transmitting data to the user terminal to display basicadditional information matched to subcontents to the user terminal, ifthe reliability of a producer of the contents is less than apredetermined threshold; and transmitting data to the user terminal todisplay at least one of basic additional information or commerceadditional information matched to subcontents to the user terminal, ifthe reliability of a producer of the contents is equal to or higher thana predetermined threshold.
 16. A method for analyzing contents,comprising: separating contents into one or more subcontents byanalyzing the contents; matching and storing additional information withthe subcontents; receiving search information from a user terminal; andsending at least one of the contents, the subcontents or the matchedadditional information corresponding to the search information to theuser terminal, wherein the separating contents into one or moresubcontents by analyzing the contents includes: identifying a feedbackinformation about the contents or a producer of the contents;determining reliability of the contents or the producer of the contentsbased on the feedback information; and separating the contents havingthe reliability equal to or higher than a predetermined threshold intoone or more subcontents.
 17. A server for analyzing contents,comprising: a processor including one or more cores; and a memory;wherein the processor is configured to separate contents into one ormore subcontents by analyzing the contents; match and store additionalinformation with the subcontents; receive search information from a userterminal; and send at least one of the contents, the subcontents or thematched additional information corresponding to the search informationto the user terminal, wherein the processor is further configured to:identify a feedback information about the contents or a producer of thecontents; determine reliability of the contents or the producer of thecontents based on the feedback information; and separate the contentshaving the reliability equal to or higher than a predetermined thresholdinto one or more subcontents.